Customer Segmentation and Review Analysis

Abstract

This paper presents a comprehensive study and practical implementation of customer segmentation and review analysis by integrating machine learning techniques with business intelligence (BI) tools. The primary objective is to enable businesses to better understand their customer base and improve strategic decision- making through data-driven insights. Utilizing technologies such as Python for data analysis and modeling, K-Means Clustering for customer segmentation, SQL for efficient data management, and Power BI for interactive data visualization, this project demonstrates a unified approach to customer analytics. The segmentation process groups customers based on key variables including purchase behavior, demographics, and frequency of engagement. This enables the creation of targeted marketing campaigns and personalized service delivery. Simultaneously, sentiment analysis is applied to customer reviews using Natural Language Processing (NLP) techniques like TextBlob and VADER to interpret opinions and emotional tone from textual feedback. The results reveal the overall sentiment distribution and highlight key concerns or satisfaction points expressed by customers. The integration of data preprocessing, clustering, sentiment extraction, and dashboard visualization into a single analytical pipeline offers a robust framework for customer intelligence. The Power BI dashboards provide stakeholders with an intuitive interface to filter insights by customer segment, sentiment category, and regional trends, facilitating agile business responses. This research highlights the potential of combining machine learning and BI tools to transform raw customer data into actionable insights that enhance both customer experience and business performance.